SoroushVahidi/scidocs huggingface dataset:
Chapter 3 Recent Developments in Auxiliary Particle Filtering
(2009)

State space models (SSMs; sometimes termed hidden Markov models, particularly in the discrete case) are very popular statistical models for time series. Such models describe the trajectory of some system of interest as an unobserved E-valued Markov chain, known as the signal process. Let X1 ∼ ν and Xn|(Xn−1 = xn−1) ∼ f(·|xn−1) denote this process. Indirect observations are available via an observation process, {Yn}n∈N. Conditional upon Xn, Yn is independent of the remainder of the observation and signal processes, with Yn|(Xn = xn) ∼ g(·|xn). For any sequence {zn}n∈N, we write zi:j = (zi, zi+1, ..., zj). In numerous applications, we are interested in estimating, recursively in time, an analytically intractable sequence of posterior distributions {p (x1:n| y1:n)}n∈N, of the form:
Fast time series classification using numerosity reduction
(2006)

Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there is a limit to how much this can help. In this work, we propose an additional technique, numerosity reduction, to speed up one-nearest-neighbor DTW. While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, we show here that we can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy. We test our ideas with a comprehensive set of experiments, and show that it can efficiently produce extremely fast accurate classifiers.
Blind Source Separation in nonminimum-phase systems based on filter decomposition
(2010)

This paper focuses on the causality problem in the task of Blind Source Separation (BSS) of speech signals in nonminimum-phase mixing channels. We propose a new algorithm for solving this problem using filter decomposition approach. Our proposed algorithm uses an integrated cost function in which independence criterion is defined in frequency-domain. The parameters of demixing system are derived in time-domain, so the algorithm has the benefits of both time and frequency-domain approaches. Compared to the previous work in this framework, our proposed algorithm is the extension of filter decomposition idea in multi-channel blind deconvolution to the problem of blind source separation of speech signals. The proposed method is capable of dealing with both minimum-phase and nonminimum-phase mixing situations. Simulation results show considerable improvement in separating speech signals specially when the mixing system is nonminimum-phase.
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
(2018)

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using sidechannel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.
Parking Guidance System Based on ZigBee and Geomagnetic Sensor Technology
(2014)

Concerning the phenomenon that common parking service could not satisfy the increasing demand of the private vehicle owners, an intelligent parking guidance system based on Zig Bee network and geomagnetic sensors was designed. Real-time vehicle position and related traffic information were collected by geomagnetic sensors around parking lots and updated to center server via Zig Bee network. On the other hand, out-door Liquid Crystal Display screens controlled by center server can display information of available parking places. In this paper, guidance strategy was divided into four levels, which could provide clear and effective information to drivers. The experimental results prove that the distance detection accuracy of geomagnetic sensors was within 0.4m, and the lowest package loss rate of the wireless network in the range of 150m is 0%. This system can provide solution for better parking service in intelligent cities.
Normalized ( Pointwise ) Mutual Information in Collocation Extraction
(2009)

In this paper, we discuss the related information theoretical association measures of mutual information and pointwise mutual information, in the context of collocation extraction. We introduce normalized variants of these measures in order to make them more easily interpretable and at the same time less sensitive to occurrence frequency. We also provide a small empirical study to give more insight into the behaviour of these new measures in a collocation extraction setup.
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
(2008)

Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tasks at a variety of granularities, issuing multiple queries as they attempt to accomplish tasks. In this work we study real sessions manually labeled into hierarchical tasks, and show that timeouts, whatever their length, are of limited utility in identifying task boundaries, achieving a maximum precision of only 70%. We report on properties of this search task hierarchy, as seen in a random sample of user interactions from a major web search engine's log, annotated by human editors, learning that 17% of tasks are interleaved, and 20% are hierarchically organized. No previous work has analyzed or addressed automatic identification of interleaved and hierarchically organized search tasks. We propose and evaluate a method for the automated segmentation of users' query streams into hierarchical units. Our classifiers can improve on timeout segmentation, as well as other previously published approaches, bringing the accuracy up to 92% for identifying fine-grained task boundaries, and 89-97% for identifying pairs of queries from the same task when tasks are interleaved hierarchically. This is the first work to identify, measure and automatically segment sequences of user queries into their hierarchical structure. The ability to perform this kind of segmentation paves the way for evaluating search engines in terms of user task completion.
Query segmentation revisited
(2011)

We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they use expensive features and are difficult to be reimplemented.
 The main contribution of this paper is a new method for query segmentation that is easy to implement, fast, and that comes with a segmentation accuracy comparable to current state-of-the-art techniques. Our method uses only raw web n-gram frequencies and Wikipedia titles that are stored in a hash table. At the same time, we introduce a new evaluation corpus for query segmentation. With about 50,000 human-annotated queries, it is two orders of magnitude larger than the corpus being used up to now.
Delphi method: techniques and applications
(1975)

Cross-impact analysis is a method for revising estimated probabilities of future events in terms of estimated interactions among those events. This Report presents an elementary cross-impact model where the cross-impacts are formulated as relative probabilities. Conditions are derived for the consistency of the matrix of relative probabilities of n events. An extension also provides a necessary condition for the vector of absolute probabilities to be consistent with the relative probability matrix. An averaging technique is formulated for resolving inconsistencies in the matrix, and a nearest-point computation derived for resolving inconsistencies between the set of absolute probabilities and the matrix. Although elementary, the present model clarifies some of the conceptual problems associated with cross-impact analysis, and supplies a relatively sound basis for revising probability estimates in the limited case where interactions can be approximated by relative probabilities.
Phishing e-mail detection by using deep learning algorithms
(2018)

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.
Bundle Adjustment in the Large
(2010)

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.
Building Caring Healthcare Systems in the Internet of Things
(2018)

The nature of healthcare and the computational and physical technologies and constraints present a number of challenges to systems designers and implementers. In spite of the challenges, there is a significant market for systems and products to support caregivers in their tasks as the number of people needing assistance grows substantially. In this paper, we present a structured approach for describing Internet of Things (IoT) for healthcare systems. We illustrate the approach for three use cases and discuss relevant quality issues that arise, in particular, the need to consider caring as a requirement.
Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain
(2012)

Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.
Entity Retrieval via Entity Factoid Hierarchy
(2015)

We propose that entity queries are generated via a two-step process: users first select entity facts that can distinguish target entities from the others; and then choose words to describe each selected fact. Based on this query generation paradigm, we propose a new entity representation model named as entity factoid hierarchy. An entity factoid hierarchy is a tree structure composed of factoid nodes. A factoid node describes one or more facts about the entity in different information granularities. The entity factoid hierarchy is constructed via a factor graph model, and the inference on the factor graph is achieved by a modified variant of Multiple-try Metropolis algorithm. Entity retrieval is performed by decomposing entity queries and computing the query likelihood on the entity factoid hierarchy. Using an array of benchmark datasets, we demonstrate that our proposed framework significantly improves the retrieval performance over existing models.
A Foray into Conficker's Logic and Rendezvous Points
(2009)

We present an in depth static analysis of the Conficker worm, primarily through the exploration of the client-side binary logic. In this paper, we summarize various aspects of the inner workings of binary variants A and B,1 which were the first in a chain of recent revisions aimed to keep this epidemic resistant to ongoing eradication attempts. These first two variants have combined to produce a multi-million node population of infected hosts, whose true main purpose has yet to be fully understood. We further validate aspects of our analysis through in-situ network analyses, and discuss some attribution links about its origins.
Information Security Threats Classification Pyramid
(2010)

Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.
Collaborative privacy management: mobile privacy beyond your own devices
(2014)

As the development of mobile devices and applications, mobile privacy has become a very important issue. Current researches on mobile privacy mainly focus on potential leakages on a particular device. However, leakage of sensitive data on a mobile device not only violates the privacy of the phone (or data) owner, but also violates the privacy of many other people whose information are contained in the data directly or indirectly (they are called data involvers). To address such problems, we introduce a collaborative privacy management framework, which aims to provide fine-grained data privacy protection for both data owners and data involvers in a distributed manner. Based on individual privacy policies specified by each user, a collaborative privacy policy is generated and enforced on different devices automatically. As a proof-of-concept prototype, we implement the proposed framework on Android and demonstrate its applicability with two case studies.
Adding nesting structure to words
(2006)

We propose the model of nested words for representation of data with both a linear ordering and a hierarchically nested matching of items. Examples of data with such dual linear-hierarchical structure include executions of structured programs, annotated linguistic data, and HTML/XML documents. Nested words generalize both words and ordered trees, and allow both word and tree operations. We define nested word automata—finite-state acceptors for nested words, and show that the resulting class of regular languages of nested words has all the appealing theoretical properties that the classical regular word languages enjoys: deterministic nested word automata are as expressive as their nondeterministic counterparts; the class is closed under union, intersection, complementation, concatenation, Kleene-*, prefixes, and language homomorphisms; membership, emptiness, language inclusion, and language equivalence are all decidable; and definability in monadic second order logic corresponds exactly to finite-state recognizability. We also consider regular languages of infinite nested words and show that the closure properties, MSO-characterization, and decidability of decision problems carry over.
 The linear encodings of nested words give the class of visibly pushdown languages of words, and this class lies between balanced languages and deterministic context-free languages. We argue that for algorithmic verification of structured programs, instead of viewing the program as a context-free language over words, one should view it as a regular language of nested words (or equivalently, a visibly pushdown language), and this would allow model checking of many properties (such as stack inspection, pre-post conditions) that are not expressible in existing specification logics.
 We also study the relationship between ordered trees and nested words, and the corresponding automata: while the analysis complexity of nested word automata is the same as that of classical tree automata, they combine both bottom-up and top-down traversals, and enjoy expressiveness and succinctness benefits over tree automata.
Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management
(2018)

This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.
Hybrid digital and analog beamforming design for large-scale MIMO systems
(2015)

Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.
Design and implementation of embedded Web server based on arm and Linux
(2010)

This paper achieves the design of an embedded Web server, which takes ARM920T-S3c2410s chip as its core and Linux as its operating system. This is because Linux can be reduced and transplanted. The method used to transplant Web server Boa on the embedded Linux platform is also discussed in detail, and through CGI technology functions of dynamic Web page is successfully realized. Relevant experiments show that after the Web server is embedded into the network video monitoring system, dynamic page interaction can be achieved between the Web server and the embedded system via the browser in the Windows environment.